{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Procedures and Functions Tutorial\n",
    "\n",
    "MLDB is the Machine Learning Database, and all machine learning operations are done via Procedures and Functions. Training a model happens via Procedures, and applying a model happens via Functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The notebook cells below use `pymldb`'s `Connection` class to make [REST API](../../../../doc/#builtin/WorkingWithRest.md.html) calls. You can check out the [Using `pymldb` Tutorial](../../../../doc/nblink.html#_tutorials/Using pymldb Tutorial) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pymldb import Connection\n",
    "mldb = Connection(\"http://localhost\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading a Dataset \n",
    "\n",
    "The classic [Iris Flower Dataset](http://en.wikipedia.org/wiki/Iris_flower_data_set) isn't very big but it's well-known and easy to reason about so it's a good example dataset to use for machine learning examples.\n",
    "\n",
    "We can import it directly from a remote URL:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/import_iris</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:12.7195733Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "        <span style=\"color: #333333; font-weight: bold\">&quot;numLineErrors&quot;</span>: <span style=\"color: #0000dd\">0</span>\n",
       "      }, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:13.0135105Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:12.719491Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;headers&quot;</span>: [\n",
       "        <span style=\"color: #0000dd\">&quot;sepal length&quot;</span>, \n",
       "        <span style=\"color: #0000dd\">&quot;sepal width&quot;</span>, \n",
       "        <span style=\"color: #0000dd\">&quot;petal length&quot;</span>, \n",
       "        <span style=\"color: #0000dd\">&quot;petal width&quot;</span>, \n",
       "        <span style=\"color: #0000dd\">&quot;class&quot;</span>\n",
       "      ], \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;outputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;iris&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;dataFileUrl&quot;</span>: <span style=\"color: #0000dd\">&quot;https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data&quot;</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;import.text&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;import_iris&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;import.text&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;import_iris&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/import_iris', {\n",
    "    \"type\": \"import.text\",\n",
    "    \"params\": {\n",
    "        \"dataFileUrl\": \"http://public.mldb.ai/iris.data\",\n",
    "        \"headers\": [ \"sepal length\", \"sepal width\", \"petal length\", \"petal width\", \"class\" ],\n",
    "        \"outputDataset\": \"iris\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A quick look at the data\n",
    "\n",
    "We can use the [Query API](../../../../doc/#builtin/sql/QueryAPI.md.html) to get the data into a Pandas DataFrame to take a quick look at it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length</th>\n",
       "      <th>sepal width</th>\n",
       "      <th>petal length</th>\n",
       "      <th>petal width</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sepal length  sepal width  petal length  petal width  \\\n",
       "_rowName                                                         \n",
       "97                 5.7          2.9           4.2          1.3   \n",
       "11                 5.4          3.7           1.5          0.2   \n",
       "112                6.4          2.7           5.3          1.9   \n",
       "134                6.3          2.8           5.1          1.5   \n",
       "142                6.9          3.1           5.1          2.3   \n",
       "\n",
       "                    class  \n",
       "_rowName                   \n",
       "97        Iris-versicolor  \n",
       "11            Iris-setosa  \n",
       "112        Iris-virginica  \n",
       "134        Iris-virginica  \n",
       "142        Iris-virginica  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mldb.query(\"select * from iris\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f23b54d3410>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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B58GD7kUzs+ZVYOT4R8x8Q3FNnP5WmzL+EVto1uGjU91oauvFdLMO2e2fYbCp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f23921b6410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns, pandas as pd\n",
    "\n",
    "sns.pairplot(df, hue=\"class\", size=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Machine Learning with a `kmeans.train` Procedure\n",
    "\n",
    "We will create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`kmeans.train`](../../../../doc/#builtin/procedures/KmeansProcedure.md.html). This will train an unsupervised K-Means model and use it to assign each row in the input to a cluster, in the output dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/iris_train_kmeans</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:18.0258212Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:18.030994Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:18.025736Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;trainingData&quot;</span>: <span style=\"color: #0000dd\">&quot;select * EXCLUDING(class) from iris&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;metric&quot;</span>: <span style=\"color: #0000dd\">&quot;euclidean&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;outputDataset&quot;</span>: <span style=\"color: #0000dd\">&quot;iris_clusters&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;numClusters&quot;</span>: <span style=\"color: #0000dd\">3</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;kmeans.train&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;iris_train_kmeans&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;kmeans.train&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;iris_train_kmeans&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/iris_train_kmeans', {\n",
    "    'type' : 'kmeans.train',\n",
    "    'params' : {\n",
    "        'trainingData' : 'select * EXCLUDING(class) from iris',\n",
    "        'outputDataset' : 'iris_clusters',\n",
    "        'numClusters' : 3,\n",
    "        'metric': 'euclidean',\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the output dataset and compare the clusters the model learned with the three types of flower in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Iris-setosa</th>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <th>Iris-virginica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>_rowName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[0]</th>\n",
       "      <td>50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[1]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>48</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Iris-setosa  Iris-versicolor  Iris-virginica\n",
       "_rowName                                              \n",
       "[0]                50              NaN             NaN\n",
       "[1]               NaN                2              36\n",
       "[2]               NaN               48              14"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.query(\"\"\"\n",
    "    select pivot(class, num) as *\n",
    "    from (\n",
    "        select cluster, class, count(*) as num\n",
    "        from merge(iris_clusters, iris)\n",
    "        group by cluster, class\n",
    "    )\n",
    "    group by cluster\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the K-means algorithm doesn't do a great job of clustering this data (as is mentioned in the Wikipedia article!)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Machine Learning with `classifier.train` and `.test` Procedures\n",
    "\n",
    "We will now create and run a [Procedure](../../../../doc/#builtin/procedures/Procedures.md.html) of type [`classifier.train`](../../../../doc/#builtin/procedures/Classifier.md.html). The configuration below will use 20% of the data to train a decision tree to classify rows into the three classes of Iris. The output of this procedure is a [Function](../../../../doc/#builtin/functions/Functions.md.html), which we will be able to call from REST or SQL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>PUT http://localhost/v1/procedures/iris_train_classifier</strong><br /><strong style=\"color: green;\">201 Created</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;status&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;firstRun&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runStarted&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:18.0753982Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runFinished&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:18.0805328Z&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;2016-03-22T16:20:18.075322Z-5bc7042b732cb41f&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;finished&quot;</span>\n",
       "    }\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;config&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;params&quot;</span>: {\n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;functionName&quot;</span>: <span style=\"color: #0000dd\">&quot;iris_classify&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;trainingData&quot;</span>: <span style=\"color: #0000dd\">&quot;\\n            select \\n                {* EXCLUDING(class)} as features, \\n                class as label \\n            from iris \\n            where rowHash() % 5 = 0\\n        &quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;modelFileUrl&quot;</span>: <span style=\"color: #0000dd\">&quot;file://models/iris.cls&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;runOnCreation&quot;</span>: <span style=\"color: #0000dd\">true</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;mode&quot;</span>: <span style=\"color: #0000dd\">&quot;categorical&quot;</span>, \n",
       "      <span style=\"color: #333333; font-weight: bold\">&quot;algorithm&quot;</span>: <span style=\"color: #0000dd\">&quot;dt&quot;</span>\n",
       "    }, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;classifier.train&quot;</span>, \n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;iris_train_classifier&quot;</span>\n",
       "  }, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;state&quot;</span>: <span style=\"color: #0000dd\">&quot;ok&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;type&quot;</span>: <span style=\"color: #0000dd\">&quot;classifier.train&quot;</span>, \n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;id&quot;</span>: <span style=\"color: #0000dd\">&quot;iris_train_classifier&quot;</span>\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [201]>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.put('/v1/procedures/iris_train_classifier', {\n",
    "    'type' : 'classifier.train',\n",
    "    'params' : {\n",
    "        'trainingData' : \"\"\"\n",
    "            select \n",
    "                {* EXCLUDING(class)} as features, \n",
    "                class as label \n",
    "            from iris \n",
    "            where rowHash() % 5 = 0\n",
    "        \"\"\",\n",
    "        \"algorithm\": \"dt\",\n",
    "        \"modelFileUrl\": \"file://models/iris.cls\",\n",
    "        \"mode\": \"categorical\",\n",
    "        \"functionName\": \"iris_classify\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now test the classifier we just trained on the subset of the data we didn't use for training. To do so we use a procedure of type [`classifier.test`](../../../../doc/#builtin/procedures/Accuracy.md.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [201]>\n"
     ]
    }
   ],
   "source": [
    "rez = mldb.put('/v1/procedures/iris_test_classifier', {\n",
    "    'type' : 'classifier.test',\n",
    "    'params' : {\n",
    "        'testingData' : \"\"\"\n",
    "            select \n",
    "                iris_classify({\n",
    "                    features: {* EXCLUDING(class)}\n",
    "                }) as score,\n",
    "                class as label \n",
    "            from iris \n",
    "            where rowHash() % 5 != 0\n",
    "        \"\"\",\n",
    "        \"mode\": \"categorical\",\n",
    "        \"runOnCreation\": True\n",
    "    }\n",
    "})\n",
    "\n",
    "runResults = rez.json()[\"status\"][\"firstRun\"][\"status\"]\n",
    "print rez"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The procedure returns a confusion matrix, which you can compare with the one that resulted from the K-means procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>predicted</th>\n",
       "      <th>Iris-setosa</th>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <th>Iris-virginica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count                               \n",
       "predicted       Iris-setosa Iris-versicolor Iris-virginica\n",
       "actual                                                    \n",
       "Iris-setosa              40               0              0\n",
       "Iris-versicolor           0              37              2\n",
       "Iris-virginica            0               6             38"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(runResults[\"confusionMatrix\"])\\\n",
    "    .pivot_table(index=\"actual\", columns=\"predicted\", fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the decision tree does a much better job of classifying the data than the K-means model, using 20% of the examples as training data.\n",
    "\n",
    "The procedure also returns standard classification statistics on how the classifier performed on the test set. Below are performance statistics for each label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>0.902439</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.948718</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        f  precision    recall  support\n",
       "Iris-setosa      1.000000   1.000000  1.000000       40\n",
       "Iris-versicolor  0.902439   0.860465  0.948718       39\n",
       "Iris-virginica   0.904762   0.950000  0.863636       44"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(runResults[\"labelStatistics\"]).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They are also available, averaged over all labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weightedStatistics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.934997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precision</th>\n",
       "      <td>0.937871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recall</th>\n",
       "      <td>0.934959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>123.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           weightedStatistics\n",
       "f                    0.934997\n",
       "precision            0.937871\n",
       "recall               0.934959\n",
       "support            123.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict({\"weightedStatistics\": runResults[\"weightedStatistics\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scoring new examples\n",
    "\n",
    "We can call the Function REST API endpoint to classify a never-before-seen set of measurements like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>GET http://localhost/v1/functions/iris_classify/application?input=%7B%22features%22%3A+%7B%22sepal+width%22%3A+4%2C+%22petal+width%22%3A+2%2C+%22petal+length%22%3A+1%2C+%22sepal+length%22%3A+3%7D%7D</strong><br /><strong style=\"color: green;\">200 OK</strong><br /> <div class=\"highlight\"><pre style=\"line-height: 125%\"><span></span>{\n",
       "  <span style=\"color: #333333; font-weight: bold\">&quot;output&quot;</span>: {\n",
       "    <span style=\"color: #333333; font-weight: bold\">&quot;scores&quot;</span>: [\n",
       "      [\n",
       "        <span style=\"color: #0000dd\">&quot;\\&quot;Iris-setosa\\&quot;&quot;</span>, \n",
       "        [\n",
       "          <span style=\"color: #0000dd\">1</span>, \n",
       "          <span style=\"color: #0000dd\">&quot;-Inf&quot;</span>\n",
       "        ]\n",
       "      ], \n",
       "      [\n",
       "        <span style=\"color: #0000dd\">&quot;\\&quot;Iris-versicolor\\&quot;&quot;</span>, \n",
       "        [\n",
       "          <span style=\"color: #0000dd\">0</span>, \n",
       "          <span style=\"color: #0000dd\">&quot;-Inf&quot;</span>\n",
       "        ]\n",
       "      ], \n",
       "      [\n",
       "        <span style=\"color: #0000dd\">&quot;\\&quot;Iris-virginica\\&quot;&quot;</span>, \n",
       "        [\n",
       "          <span style=\"color: #0000dd\">0</span>, \n",
       "          <span style=\"color: #0000dd\">&quot;-Inf&quot;</span>\n",
       "        ]\n",
       "      ]\n",
       "    ]\n",
       "  }\n",
       "}\n",
       "</pre></div>\n"
      ],
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mldb.get('/v1/functions/iris_classify/application', input={\n",
    "    \"features\":{\n",
    "        \"petal length\": 1,\n",
    "        \"petal width\": 2,\n",
    "        \"sepal length\": 3,\n",
    "        \"sepal width\": 4\n",
    "    }\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Where to next?\n",
    "\n",
    "Check out the other [Tutorials and Demos](../../../../doc/#builtin/Demos.md.html).\n",
    "\n",
    "You can also take a look at the [`classifier.experiment`](../../../../doc/#builtin/procedures/ExperimentProcedure.md.html) procedure type that can be used to train and test a classifier in a single call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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